A Cloud Detection Scheme for the Chinese Carbon Dioxide Observation Satellite(TANSAT)

Cloud detection is an essential preprocessing step for retrieving carbon dioxide from satellite observations of reflected sunlight. During the pre-launch study of the Chinese Carbon Dioxide Observation Satellite （TANSAT）, a cloud-screening scheme was presented for the Cloud and Aerosol Polarization Imager （CAPI）, which only performs measurements in five channels located in the visible to near-infrared regions of the spectrum. The scheme for CAPI, based on previous cloud- screening algorithms, defines a method to regroup individual threshold tests for each pixel in a scene according to the derived clear confidence level. This scheme is proven to be more effective for sensors with few channels. The work relies upon the radiance data from the Visible and Infrared Radiometer （VIRR） onboard the Chinese FengYun-3A Polar-orbiting Meteoro- logical Satellite （FY-3A）, which uses four wavebands similar to that of CAPI and can serve as a proxy for its measurements. The scheme has been applied to a number of the VIRR scenes over four target areas （desert, snow, ocean, forest） for all seasons. To assess the screening results, comparisons against the cloud-screening product from MODIS are made. The evaluation suggests that the proposed scheme inherits the advantages of schemes described in previous publications and shows improved cloud-screening results. A seasonal analysis reveals that this scheme provides better performance during warmer seasons, except for observations over oceans, where results are much better in colder seasons.

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications.This study develops machine learning-based(ML-based)cloud detection algorithms using spectral observations for the Advanced Himawari Imager(AHI)onboard the Himawari-8 geostationary satellite.Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP)are used to provide reference labels for model development and validation.We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included,and the artificial neural network(ANN)and random forest(RF)techniques are adopted for comparison.To eliminate the influences of surface conditions on cloud detection,we introduce three models with different treatments of the surface.Instead of developing independent ML-based algorithms,we add surface variables in a binary way that enhances the ML-based algorithm accuracy by~5%.Validated against CALIOP observations,we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by~5%,while at the same time,reducing misjudgment by~3%.The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels.Overall,our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations.We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.

Cloud Detection and Centroid Extraction of Laser Footprint Image of GF-7 Satellite Laser Altimetry

The laser altimeter loaded on the GaoFen-7(GF-7)satellite is designed to record the full waveform data and footprint image,which can obtain high-precision elevation control points for stereo image.The footprint camera equipped on the GF-7 laser altimetry system can capture the energy distribution at the time of laser emission and the image of the ground object where the laser falls,which can be used to judge whether the laser is affected by the cloud.At the same time,the centroid of laser spot on the footprint image can be extracted to monitor the change of laser pointing stability.In this manuscript,a data quality analysis scheme of laser altimetry based on footprint image is presented.Firstly,the cloud detection of footprint image is realized based on deep learning.The fusion result of the model is about 5%better than that of the traditional cloud detection algorithm,which can quickly and accurately determine whether the laser spot is affected by cloud.Secondly,according to the characteristics of footprint image,a threshold constrained ellipse fitting method for extracting the centroid of laser spot is proposed to monitor the pointing stability of long-period lasers.Based on the above method,the change of laser spot centroid since GF-7 satellite was put into operation is analyzed,and the conclusions obtained have certain reference significance for the quality control of satellite laser altimetry data and the analysis of pointing angle stability.

A Cloud Detection Method for Landsat 8 Satellite Remote Sensing Images Based on Improved CDNet Model

Cloud detection in remote sensing images is a crucial task in various applications,such as meteorological disaster prediction and earth resource exploration,which require accurate cloud identi¯cation.This work proposes a cloud detection model based on the Cloud Detection neural Network(CDNet),incorporating a fusion mechanism of channel and spatial attention.Depthwise separable convolution is adopted to achieve a lightweight network model and enhance the e±ciency of network training and detection.In addition,the Convolutional Block Attention Module(CBAM)is integrated into the network to train the cloud detection model with attention features in channel and spatial dimensions.Experiments were conducted on Landsat 8 imagery to validate the proposed improved CDNet.Averaged over all testing images,the overall accuracy(OA),mean Pixel Accuracy(mPA),Kappa coe±cient and Mean Intersection over Union(MIoU)of improved CDNet were 96.38%,81.18%,96.05%,and 84.69%,respectively.Those results were better than the original CDNet and DeeplabV3+.Experiment results show that the improved CDNet is e®ective and robust for cloud detection in remote sensing images.

A cloud detection method based on a time series of MODIS surface reflectance images

The Moderate Resolution Imaging Spectroradiometer(MODIS)-Terra surface reflectance product(MOD09A1),with bands 1 to 7,is a gridded,eight-day composite product derived from the MODIS-Terra top of atmosphere reflectance swaths.It performs cloud detection and corrects for the effects of atmospheric gases and aerosols.The cloud mask(CM)algorithms for MODIS are based on empirical thresholds on spectral reflectance and brightness temperature.Since the spatial resolution of the thermal band is 1000 m,while that of MOD09A1 is 500 m,many undetected and false clouds are observed in MOD09A1.These errors always result in temporal and spatial inconsistencies in higher-level products.In this paper,a cloud detection algorithm(TSCD)based on a MOD09A1 time series is introduced.Time series cloud detection(TSCD)algorithm is based on the relative stability of ground reflectance and the sudden variations in reflectance that result from cloud cover.The algorithm first searches the clear-sky reference data,and then discriminates clouded and unclouded pixels by detecting a sudden change of reflectance in the blue wavelength and spectral correlation coefficient at the pixel level.Compared with cloud cover assessments obtained from MODIS’original CM,TSCD provides similar or better discrimination in most situations when the land surface changes slowly.

Development and Initial Assessment of a New Land Index for Microwave Humidity Sounder Cloud Detection

This paper describes a new quality control （QC） scheme for microwave humidity sounder （MHS） data assimilation. It consists of a cloud detection step and an O-B （i.e., differences of brightness temperatures between observations and model simulations） check. Over ocean, cloud detection can be carried out based on two MHS window channels and two Advanced Microwave Sounding Unit-A （AMSU-A） window channels, which can be used for obtaining cloud ice water path （IWP） and liquid water path （LWP）, respectively. Over land, cloud detection of microwave data becomes much more challenging due to a much larger emission contribution from land surface than that from cloud. The current MHS cloud detection over land employs an 0-]3 based method, which could fail to identify cloudy radiances when there is mismatch between actual clouds and model clouds. In this study, a new MHS observation based index is developed for identifying MHS cloudy radiances over land. The new land index for cloud detection exploits the large variability of brightness temperature observations among MHS channels over different clouds, It is shown that those MHS cloudy radiances that were otherwise missed by the current O-B based QC method can be successfully identified by the new land index. An O-B check can then be employed to the remaining data after cloud detection to remove additional outliers with model simulations deviated greatly from observations. It is shown that MHS channel correlations are significantly reduced by the newly proposed QC scheme.

CLOUD IMAGE DETECTION BASED ON MARKOV RANDOM FIELD

In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.

A Precipitation Detection Method for MWTS-Ⅱ Radiance Assimilation in Typhoon Simulation

FY-3C Microwave Temperature SounderⅡ(MWTS-Ⅱ)lacks observations at 23.8 GHz,31 GHz and 89 GHz,making it difficult to remove the data contaminated by precipitation in assimilation.In this paper,a fast forward operator based on the Community Radiative Transfer Model(CRTM)was used to analyze the relationship between the observation minus background simulation(O-B)and the cloud fractions in different MWTS-Ⅱchannels.In addition,based on the community Gridpoint Statistical Interpolation(GSI)system,the radiation brightness temperature of the MWTS-Ⅱwas assimilated in the regional Numerical Weather Prediction(NWP)model.In the process of assimilation,Visible and Infrared Radiometer(VIRR)cloud detection products were matched to MWTS-Ⅱpixels for precipitation detection.For typhoon No.18 in 2014,impact tests of MWTS-Ⅱdata assimilation was carried out.The results show that,though the bias observation minus analysis(O-A)of assimilated data can be reduced by quality control only with|O-B|<3 K;however,the O-A becomes much smaller while the precipitation detection is performed with Fvirr<0.9(VIRR cloud fraction threshold of 0.9).Besides,the change of the environmental field around the typhoon is more conducive to make the simulated track closer to the observation.The 72-hour typhoon track simulation error also shows that,after the precipitation detection,the error of simulated typhoon track is significantly reduced,which reflects the validity of a precipitation detection method based on a double criterion of|O-B|<3 K and Fvirr<0.9.

An Algorithm for Detecting Ice Cloud at Different Altitudes by Combining Dual CrIS Full Spectrum Resolution CO2 Channels

Using infrared sensors to detect ice clouds in different atmospheric layers is still a challenge.The different scattering and absorption properties of longwave and shortwave infrared channels can be utilized to fulfill this purpose.In this study,the release of Suomi-NPP Cross-track Infrared Sounder(Cr IS)full spectrum resolution is used to select and pair channels from longwave(~15μm)and shortwave(~4.3μm)CO2 absorption bands under stricter conditions,so as to better detect ice clouds.Besides,the differences of the weighting function peaks and cloud insensitive level altitudes of the paired channels are both within 50 h Pa so that the variances due to atmospheric conditions can be minimized.The training data of clear sky are determined by Visible Infrared Imaging Radiometer Suite(VIIRS)cloud mask product and used to find the linear relationship between the paired longwave and shortwave CO2 absorption channels.From the linear relationship,the so-called cloud emission and scattering index(CESI)is derived to detect ice clouds.CESI clearly captures the center and the ice cloud features of the Super Typhoon Hato located above 415 h Pa.Moreover,the CESI distributions agree with cloud top pressure from the VIIRS in both daytime and nighttime in different atmospheric layers.

Ground-Based Cloud Using Exponential Entropy/Exponential Gray Entropy and UPSO

Objective and accurate classification model or method of cloud image is a prerequisite for accurate weather monitoring and forecast.Thus safety of aircraft taking off and landing and air flight can be guaranteed.Thresholding is a kind of simple and effective method of cloud classification.It can realize automated ground-based cloud detection and cloudage observation.The existing segmentation methods based on fixed threshold and single threshold cannot achieve good segmentation effect.Thus it is difficult to obtain the accurate result of cloud detection and cloudage observation.In view of the above-mentioned problems,multi-thresholding methods of ground-based cloud based on exponential entropy/exponential gray entropy and uniform searching particle swarm optimization(UPSO)are proposed.Exponential entropy and exponential gray entropy make up for the defects of undefined value and zero value in Shannon entropy.In addition,exponential gray entropy reflects the relative uniformity of gray levels within the cloud cluster and background cluster.Cloud regions and background regions of different gray level ranges can be distinguished more precisely using the multi-thresholding strategy.In order to reduce computational complexity of original exhaustive algorithm for multi-threshold selection,the UPSO algorithm is adopted.It can find the optimal thresholds quickly and accurately.As a result,the real-time processing of segmentation of groundbased cloud image can be realized.The experimental results show that,in comparison with the existing groundbased cloud image segmentation methods and multi-thresholding method based on maximum Shannon entropy,the proposed methods can extract the boundary shape,textures and details feature of cloud more clearly.Therefore,the accuracies of cloudage detection and morphology classification for ground-based cloud are both improved.

Derivation of Regression Coefficients for Sea Surface Temperature Retrieval over East Asia

Among the regression-based algorithms for deriving SST from satellite measurements, regionally optimized algorithms normally perform better than the corresponding global algorithm. In this paper, three algorithms are considered for SST retrieval over the East Asia region （15°-55°N, 105°-170°E）, including the multi-channel algorithm （MCSST）, the quadratic algorithm （QSST）, and the Pathfinder algorithm （PFSST）. All algorithms are derived and validated using collocated buoy and Geostationary Meteorological Satellite （GMS-5） observations from 1997 to 2001. An important part of the derivation and validation of the algorithms is the quality control procedure for the buoy SST data and an improved cloud screening method for the satellite brightness temperature measurements. The regionally optimized MCSST algorithm shows an overall improvement over the global algorithm, removing the bias of about -0.13℃ and reducing the root-mean-square difference （rmsd） from 1.36℃ to 1.26℃. The QSST is only slightly better than the MCSST. For both algorithms, a seasonal dependence of the remaining error statistics is still evident. The Pathfinder approach for deriving a season-specific set of coefficients, one for August to October and one for the rest of the year, provides the smallest rmsd overall that is also stable over time.

Bayes-Based ARP Attack Detection Algorithm for Cloud Centers

To address the issue of internal network security, Software-Defined Network（SDN） technology has been introduced to large-scale cloud centers because it not only improves network performance but also deals with network attacks. To prevent man-in-the-middle and denial of service attacks caused by an address resolution protocol bug in an SDN-based cloud center, this study proposed a Bayes-based algorithm to calculate the probability of a host being an attacker and further presented a detection model based on the algorithm. Experiments were conducted to validate this method.

3-D Lightning Location Solution and Precision Analysis of Cloud Flash

Using the spatial coordinates of detection stations and the time of arrival of lightning wave, the observation equations can be expressed. For the large lightning detection network, the least square method is used to process the adjustment of observation data to find the most probable value of lightning position, and the result is assessed by the mean error and dilution of precision. Lightning location precision is affected by figure factor. The conclusion can be used in the design of location network, data processing, and data analysis.

MobSafe:Cloud Computing Based Forensic Analysis for Massive Mobile Applications Using Data Mining

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win＋Intel alliance in PC, Android＋ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app＇s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework （ASEF） and Static Android Analysis Framework （SAAF）, the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.